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True social insights, as opposed to social findings or social observations, have the potential to inform, shape or drive marketing and even business strategy decisions, not just social strategy decisions. Discovering that tweeting with a link on Tuesday between 10:00 – 11:00 AM drives higher levels of engagement is a social finding, not an insight.

Social media is a microcosm of the larger Big Data problem/opportunity – too much data, not enough insights. Or if you prefer, too much noise, not enough signal. If you want to improve your ability to discover insights, here are three simple approaches you can take to improve your insight hunting.

First, start all analysis with a hypothesis or series of questions the analysis is designed to answer. It is much easier to prove or disprove a hypothesis, or answer specific questions, than it is to “find out what people are saying about us in social media”. The more specific the request, the better the answer is going to be. The hypothesis may be one you develop based on preliminary analysis or it may come from the ‘customer’ for the insight. Here are two examples of hypothesis:

“Conversation about us in social media is quite negative. My boss believes ‘everyone’ is aligned against us. I disagree. My hypothesis is that there is a very vocal and active minority of consumers who are posting large volumes of negative content about us. And I believe this group is a small fraction of the total number of people who post. The majority of consumers are actually neutral toward us.”

“When we look at Twitter, Facebook and Blogs we see pretty low levels of conversation about our product and the medical condition it treats. We believe there is actually a fair amount of conversation, but the conversations are occurring in Forums which may not be crawled by most social listening tools.”

While the hypothesis is a great way to begin to focus on what is important in the data, a further focusing mechanism is the second insight discovery key – the concept of targeted listening. With targeted listening we are not trying to capture all conversations that mention the brand or product. That is a ‘boil the ocean’ approach. Instead, we listen for very specific types of conversations or conversations by very specific groups of individuals within social conversations. The trick is to have the discipline to only listen to your focus areas and not be tempted to boil the ocean in hopes of finding a few pearls. Here are three examples of targeted listening strategies:

An insurance company resists the temptation to try to capture ‘all’ mentions of the brand and decides to focus only on conversations where customers are thinking about non-renewal or switching companies.

A gaming company launches a new product and listens to understand what features are being discussed, what people like most/least about the new game and to gauge their specific reactions to the cover art.

A consumer products company listens only for consumers who are actively in the purchasing process for the type of products they offer.

The third key to discovering insights is to provide context for decision-making. Remember with insights we are trying to inform, shape and guide decision-making. Context is incredibly important to making better decisions faster. Good social analysts understand how marketing and business work and how strategic alternatives might impact results. Understanding this helps you put your insights in the proper context for decision-making.

Here is an example of how context can lead to better decisions. Company X has a crisis. You are asked to do real-time listening of the crisis and help the PR team decide when and how to engage in the conversation. You come back the next day with a line chart showing a large spike in content mentioning the crisis – thousands of mentions. You know the sentiment in negative to neutral and on which channels the content appears. Unfortunately you have not given the people deciding

whether or not to engage enough information to make a decision. What information would provide the necessary context for decision-making? What questions do we need to try to answer? Here are a few:

How much above ‘normal levels’ is the spike in content? (Normative data)

How does this event compare to that event we had last year? Or, how does the event compare to competitor X who had their own crisis last year? (Comparative data) Comparisons help decision makers determine ‘how bad is bad’.

How long do we anticipate seeing negative content at relatively high levels? (Comparative data) This might be the most important question to answer to provide context and guidance for the engagement decision. If we anticipate volume will drop back to normal in a reasonable period of time, then not engaging may be a viable and effective strategy depending on the brand involved and the nature of the crisis.

Which stakeholder groups are active in the conversations? With robust social analysis we always want to look at both the post – what is being said, and the source – who is saying it. In a crisis, who is talking is particularly important.

Normative data, comparative data and examining both post and source data are all effective techniques to provide context for decision-making.

The tough part about discovering insights is there are no shortcuts and it is a human activity. No social media analysis platform that I have found has an insight button. The key barrier is lack of people who understand how to search for and discover insights. Hope these tips make you a more effective explorer.

Quite often I am asked to consult with a company on their social media listening strategy. Their initial question more times than not is about the listening platform they should use. But it is increasingly common for the questions to be more sophisticated and the ambition behind them to be much greater. Companies with experience in social listening know that it is all too easy to focus on rudimentary analysis of brand mentions and topics, Followers and Likes and never get to the truly actionable insights that lead to marketing or business actions. Experience in listening is an important element here but you also need a path to follow. I thought a maturity model approach to social media listening could provide a possible path to consider and would provide a construct that could be used in consulting with a company on their social listening strategy.

Maturity models are sort of hot – there seems to be a proliferation in the last two years or so. One that I find particularly insightful and helpful when thinking about social listening is Forrester’s Social Maturity Model. Two really important points the folks at Forrester make is that listening is not the goal, social intelligence is, and that social intelligence informs actions taken by marketing or some other area of the business. Action being the operative word here. Social intelligence is a closely related topic to social business, and if social business is more your thing the Dachis Group has an interesting social business maturity model. Big data more your bag? Check out IBM’s big data governance model. After looking at the models out there, I could not find one specific enough to social media listening so I took a stab at creating one.

Social Media Listening Maturity Model

There are five stages in the Social Media Listening Maturity Model, beginning with reactive alerts and ending with social intelligence. Let’s take a brief look at each stage and some of the overarching differences or changes one sees with social listening maturity.

Reactive Alerts – Many companies or brands begin by establishing a reactive alert system that notifies them whenever their brand is mentioned or is mentioned with specific keywords. Think Google Alerts. Companies in this stage may only periodically check social media channels to see what may have changed or is new since the last check-in.

Monitoring Social Media – At the next stage, the company has begun active monitoring of all ‘owned’ social embassies. They also are monitoring social media conversations, often focused on trying to detect any ‘bad’ news, mentions or conversations.

Companies in these first two stages generally have a reactive stance toward social media, viewing it as another way to find out about news and circumstances that may harm or otherwise impact the organization. It is common for companies in these stages to use one or more of the various free tools available to gather web and social media data.

Social Listening – The third stage is most likely where the largest percentage of companies reside today. Companies in stage three are listening to social conversations about their company, brands and products. They are tracking mentions of competitors and calculating share of conversation. Many also track issues and topics that are important to their brands/products/company. At this stage many begin to put additional emphasis on ‘who’ is talking (source) not just what is being said (post). Most companies in the social listening phase have transitioned from free tools to paid platforms.

Companies in the first three stages often suffer from having too much data and not enough insights. They are up to their necks in ‘big data’ but lack the experience and expertise to analyze the data and reduce it down to crisp, actionable insights supported by the data. They look for the Insight button on the tools they use but increasingly realize insights are the product of human analysts, not tools or data.

Strategic Listening – The transition to strategic listening brings with it a bias toward ‘listening with a purpose’. I first heard this turn of phrase from my friends at Radian6 and use it often. Listening with a purpose is just that – listening to specific sets of conversations with a specific goal or objective in mind. Often in insight work, the goal or objective may take the form of a hypothesis we are trying to test. Here are a few examples of listening with a purpose:

Listening for conversations of consumers in a particular phase of the buying decision process

Listening to customers whose subscriptions or policies are about to expire that are expressing thoughts of changing vendors

Identifying, tracking and building relationships with key influencers

Listening for consumer reactions to new packaging or product features

Mining the emotional content of specific stakeholder groups to determine potential risk around a sensitive issue.

During this phase, an Enterprise listening strategy is often developed and implemented. Some also begin to integrate data from sources beyond social media – search, web analytics and customer data for example.

Social Intelligence – Forrester defines social intelligence as the process of turning social media data into actionable marketing and business strategy. Social intelligence therefore is not about the best times to tweet or whether or not a twitter party would be an effective tactic, it is about informing strategic decisions that impact the company’s success. For me, three concepts are crucial:

Action – social intelligence is designed to drive true actions.

Integration – although the definition focuses on social media data and insights, the fact is that true insights often require more than just social data. Integrating data from multiple data sources – consumer survey, behavioral tracking, social posts, search analytics, advertising data, customer records, scan/sales data – allows for greater understanding and richer insights. Integration of multiple data types often requires multiple tools and platforms to aggregate and analyze the data.

Sharing – For social intelligence to truly take root within an organization, the data and insights should involve cross-disciplinary groups that can look at the data from different perspectives and collectively arrive at better insights than any one group could in a vacuum. The insights then need to be systematically shared broadly across the organization so they may be acted upon in a manner that will create the most impact. Social intelligence can be a catalyst to the silos within an organization tumbling down.

Since the social listening and social intelligence ‘markets’ are relatively immature, this model will continue to evolve and be refined.

Where is your company today on the social media listening maturity model?

Though almost everyone would agree that social media is about engagement and not eyeballs, too much of digital and social media measurement is focused on audience size. How many Followers do we have? How can we get a million Likes? How many unique visitors did we have to our site this month? And unfortunately, audience size estimates in social media grossly overstate the actual relevant audience. We seem fixated and oriented toward ‘how many’, while our focus should be on ‘who’ and specifically, ‘who within our target audience’. Generally speaking, the advertising industry has led the way with audience measures and is ahead of where the public relations and social media camps are with respect to level of sophistication.

In television advertising, the concept of Target Rating Points is a refinement of Gross Rating Points where you only measure and get ‘credit’ for the percentage of the gross audience that meets your target audience criteria. In an effort to keep refining the audience data available, Nielsen has evolved from diary-based data to electronic data to software at the set-top box level that allows operators to monitor channels choices and changes. In audio-based media, Arbitron’s Portable People Meter recognizes today’s mobile world and begins to address cross-platform measurement. It is also interesting to reflect on the U.S. Congressional involvement in television audience ratings accuracy (or lack thereof as it were) that led to the formation of what is now known as the Media Rating Council in the early 1960’s. The time has come for social media audience research to greatly increase in sophistication, accuracy and relevance.

When we think about social media audience size measures today, the emphasis is on Opportunities To See (OTS), although almost never by this name. We might call them Impressions or Reach, but what we really mean is how many people had the potential to see this content item. There are two overarching issues here:

Opportunities to see are not the same as actually seeing

The metrics count all possible members of the audience, regardless of whether or not they are part of the targeted audience or can even buy the product or service.

OTS is also a prevalent metric in the public relations industry which has always focused on stating the highest possible audience measures. In traditional media we know the probability of any one person in the audience actually seeing the article in question is a fraction of the total audience – a reasonable estimate is 10% or less. So OTS greatly overstates the actually number of people who saw a given article. To compound the audience overstatement, we have the practice of using audience multipliers to ‘credit’ earned media for either a perceived credibility advantage over advertising or to account for pass-along circulation (see this IPR white paper for more on multipliers). Thankfully the practice of applying multipliers (and its evil cousin AVEs) is out of favor and rapidly on a path toward extinction.

In social media one can make the case the audience metrics situation is actually exacerbated in that the probability of any one follower seeing any one tweet, for example, is most likely an order of magnitude less than in earned media – my guesstimate is 1% or less. Before you call BS on this guesstimate, play around with a few Twitter factoids – the recent Pew Research study suggesting only 8% of Twitter users use it daily, the perishable nature of most individual’s twitter streams, and the fact that a reasonably high percentage of Followers of a brand are bots, and the reality is that only a small fraction of twitter followers actually see tweets, let alone find it interesting enough to share or comment on. And, of course, not all Facebook Likes see every post you make either. Riffing on the old, ‘if a tree falls in the forest…’, if you tweet into the twitterverse and no one sees it does it make an impact?

Evolving from ‘opportunities to see’ to ‘relevant audience’ measures.

Most social media campaigns have a specific target audience in mind, often described with demographics (Female, age 18 – 34), psychographics (who worry about feeding their family healthy food on a budget) and behavioral (access deal and coupon sites regularly) dimensions. Yet when it comes to reporting and measurement we take credit for the entire audience (total OTS) rather than the percentage of the audience that meets our targeting criteria. Trying to promote lingerie to 22 – 29 year old ladies? No worries, count all your Twitter Followers and all the visitors to your website – the men, the young and the old – everybody counts. Trying to sell camo clothing to male hunters? No worries, everybody counts – male, female, hunters, non-hunters and PETA members, too. Of course this all seems a little silly and strange and I suppose it would be if it wasn’t the way most social audience reporting is done today. It is unusual to see someone in social media, or PR for that matter, report only the relevant audience opportunities to see. Why is this? I believe there are three primary reasons:

Legacy – the PR industry has historically reported gross potential audience size rather than the relevant audience size. When social media came around, this same orientation toward gross audience measurement was used.

Data – there is a lack of consistent social media demographic and psychographic audience data available and it often resides in channel silos rather than cross-platforms. And often the audience data from one platform (e.g. ComScore) does not match the data available from another platform (e.g. Compete).

Standards – there are no standards for social media audience metrics and no codified best practices for audience measurement.

Where do we go from here?

First, we need a change in mindset of how we think about audiences. From ‘how many people theoretically had the potential to see our content’ to ‘how many of the people we were targeting actually saw our content’. Big audience numbers are irrelevant. Relevant audience numbers are big.

Next, as the demand for audience data that contains demographic, psychographic and behavioral data grows, it is reasonable to assume one or more of the large media data companies might start to aggregate and make the data available. Privacy concerns, cookies and other issues are also in play here.

And last but not least, industry standards for social media audience and engagement metrics and definitions are necessary for transparency and replicability that will increase credibility of social media measurement and reporting. 2012 will go down as the year that serious cross-industry progress on social media metrics standards began and gained momentum. There has already been a lot of progress (See this post from Katie Paine), and this week in Dublin at the 4th AMEC European Summit on Measurement the theme is around attempting to define standards for social media metrics and measurement. To tune into the debate as it occurs in Dublin, monitor #SMMStandards and #AMEC2012.

What are your thoughts on the need for social media metrics standards and the use of target rather than gross audience size estimates?

If you want to evaluate the robustness and effectiveness of your approach to social media measurement, ask yourself these three fundamental questions:

Does the approach measure the ‘right’ things in order to show the business impact of the programs and initiatives?

Will stakeholders of the report receive the data and actionable insights required to make strategic decisions?

Are the data and insights presented in a clear and concise manner that tells a story and makes it easy to understand and act upon?

Measuring the ‘Right’ Things

Social media metrics are derived from three primary sources:

Ideally, a robust social media measurement program will have a rich metrics set that contains metrics from all three areas. Metrics tied to program objectives allow for direct measurement of program success. Fundamentally, measurement is about assessing performance against objectives. It is surprising how often social program objectives are slanted toward channel-specific metrics (e.g. Likes or Followers) and not the specific outcomes desired for the program – what you hope to accomplish by implementing the program. Also, relying too heavily on channel metrics limits you to what you can measure rather than what you should measure. Business outcome metrics are used to connect the dots between social media programs and the business results they are designed to drive. Social programs that cannot answer, or at least address, the management question, “How is this impacting my business”, are more susceptible to resource allocation scrutiny (#pleasecutmybudget). Stated another way, if management asks how we’re doing in social media and we reply, “great, post virality is up 6.1% this month”, we make it difficult for that individual to understand how social media/business initiatives are helping move the business forward.

Getting to Data and Insights that Inform Strategic Decisions

Expectations for social media measurement and analysis have risen. In addition to sound analysis and reporting of performance against key metrics and KPIs, understanding audience dynamics and developing actionable insights are rapidly becoming de rigueur. Insights may be defined as synthesizing and interpreting data to provide actionable information and knowledge that informs strategic decisions. Too many social media measurement programs take a social-centric rather than a business-centric approach to insights. They often feature insights and recommendations that are tactical in nature – the best time of day or how many times to tweet, or what type of content seems to be most successful. Ideally, insights and recommendations in social measurement reports would be operating one level above this, informing strategic decisions about how social programs and conversations are impacting, or could impact, the business. To do this requires an understanding of the business function (e.g. marketing, customer service) impacted by the social program and an ability to ask the right questions prior to starting a social media analysis.

For example, let’s say Company X plans to introduce a new video game. A social listening program has been implemented to analyze the early consumer reaction to the game. Based on the listening analysis, changes to the packaging, marketing or even the product itself are possible. If you are in charge of the marketing campaign for the game, what are the types of social media insights you need to make decisions about the game and the marketing campaign?

What is the level of buzz about the game? What is the overall sentiment? How does this compare to previous game launches?

What are people talking about in social media – availability, cost, specific features of the game, packaging, marketing campaign?

What features of the game do consumers seem to like most? Least? Specifically, what do they like or dislike?

What are the most influential gaming enthusiasts saying about the product?

Who are the promoters and detractors? What is the ratio of promoters to detractors? How does this compare to promoters and detractors from previous game launches?

How much social media conversation contains recommendations or expresses purchase intent? How does this compare to previous launches?

Dashboards have gotten a bit of a bad rap – not because dashboards are not useful, but because some have used them as THE measurement report rather than just one aspect of a good report. I’m a dashboard proponent for a few reasons:

Deciding which metrics to feature on a dashboard is a good strategic exercise requiring you to focus on the very most important and relevant metrics for the intended audience

Online, dynamic dashboards are an effective user interface that can be used as a launching- off point for drilling into data to understand the underlying story

Good dashboards present a snapshot of overall performance that is easily absorbed and understood.

A dashboard-driven social media measurement report is versatile and effective in many situations. A typical report might consist of one of more dashboards and then a deeper dive on each of the key metrics featured on the dashboards, along with audience insights, strategic insights and recommendations. This format provides a quick snapshot (dashboard) of results, ideal for those stakeholders interested only in topline data, and provides sufficient depth to satisfy those more interested in the underlying drivers of the metric

Social media measurement programs that are built around metrics tied to business outcomes and show how programs are performing against objectives are important. Reports that deliver clear insights that inform strategic decisions are important. And delivering those reports in a compelling format that enhances usability and effectiveness is important. How do your programs stack up?

The emphasis on influencer marketing in social media has reached a fever pitch in 2011 and with it a flood of tools and opinions on how to navigate the influence waters. This is interesting in that one of the most powerful aspects of social media marketing is the ability to establish connections and relationships directly with prospects and customers and not have to go through an intermediary to communicate. But we’ll leave that to the social strategists to reconcile and justify. Influencer marketing is hardly a new strategy. Through the years, much work in traditional public relations utilized influencer targeting (e.g. market analysts, financial analysts, KOLs, other customers) to help amplify and endorse a brand or a company’s products and services. So why is there so much discussion and confusion about influence in social media? Let’s explore.

Influence Basics

A definition I like for influence is: effecting change in another person’s attitudes, opinions, beliefs and/or behavior. I believe the most overlooked word in this definition is change. Without change influence has not truly occurred. One challenge here is influence can happen without any resulting short-term observable action. Influence takes hold primarily between the ears, not necessarily with hand on mouse or wallet. This creates fundamental challenges when trying to measure the degree to which influence has occurred.

Another challenge we face is that influence is contextual not absolute. People who influence others do so primarily in areas where they have specific expertise or authority. It is common to be influential in one area but have little or no influence in others. One of the main issues with current influence tools are they do a relatively poor job of establishing contextual relevance.

The distinction between creating influence within a target audience and who/what has influence over the target has a tendency to get muddled. To clarify, determining who has the potential to influence the target audience, (the influencers), is a targeting question. Have we created influence, (changed attitudes, opinions, beliefs and/or behavior) is a measurement question.

Influence is purposeful. In real life or digital life, when we set out to change the opinion, attitude, beliefs or behavior of another person or group, we do so with a downstream motivation – for them to take a specific action. The list of possible actions is lengthy – buy a product, visit a website, tell a friend, vote, wave a sign and donate to name a few. Of course, not all desired actions are equal in terms of amount of influence required for change. Opinions might be easier to change than an attitude. An attitude is easier to change than a belief. Behavioral change can range from relatively easy to nearly impossible depending on the specific behavior. In marketing, the ultimate behavior or action we try to influence is purchase behavior. It is important to think through the specific actions you hope the target will take as a result of being influenced. This is also the sweet spot for influence measurement.

While creating an action/behavior change is the ultimate reason for influencing someone, it is helpful to think of the process of influence as two stages – opinion, attitude or belief change – and then, because of this change, did an action occur or was a behavior changed. Stated another way, the opinion change is an intermediate or micro outcome and the desired action is a final or macro outcome. Depending on the type of purchase decision there may be a time lag between the micro and macro outcomes that make it difficult to connect the dots. In his book The Business of Influence, Philip Sheldrake presents a concept called the “Maturity of Influence Approach”. Basically it melds two important concepts to use when thinking about influence measurement – focus on the influence, not the influencer (Philip refers to this as “influence-centric), and to start at the macro outcome/action and trace the path of influence back to the source(s) of influence. One simple example of this in a B2B context would be to ask the prospect at the time they are ready to make a purchase, “what sources of information or opinion were most valuable to you in making your decision to buy our product?” A similar question or two can be asked using a pop-up survey in an ecommerce situation.

Influence and Engagement Confusion

A primary source of influence confusion is failing to distinguish between a simple act of engagement and the process of being influenced. If someone in my Twitter stream sends out a tweet and I retweet it, have they influenced me to retweet or have I simply engaged with that individual’s content? Many who have written about social media influence have suggested that in RTing the tweet, I have been influenced to do so. I do not believe that is the case. I have engaged with the content, but have there been any true changes in my attitudes, opinions, beliefs or behavior? Again, the operative word here is change. Does the act of RTing constitute a behavioral change? Probably not. Engagement – yes, influence – no.

Engagement is a necessary pre-condition to Influence. (This social media measurement model addresses the distinction) Without engagement you don’t have the opportunity to influence. Influence, however, only occurs if that engagement leads to a change in attitudes, opinions, beliefs and behavior.

Influence, Popularity and Celebrity Confusion

There also seems to be some confusion about the difference between influence, popularity and celebrity. Although related, and in some cases overlapping, they are three distinct concepts. In my opinion, at least some of the confusion stems from Klout and other influencer tools that seem to measure popularity but call it influence. So what is the difference?

Popularity is the state of being popular – widely admired, accepted or sought after.

Celebrity is a famous person, renown and fame.

If popularity is about being well-liked and celebrity is about being well-known, influence is more about being well-respected, with associations like knowledge, persuasion and trust. Some of the confusion lies in the fact that some celebrities do have influence over the types of behaviors that make the cash register ring. Oprah comes to mind. Other celebrities, while very popular, don’t really have the ability to create meaningful influence. They can get content re-tweeted (WINNING!) but do they have any influence over the types of actions brands really value?

Keeping Online Influence in Perspective

As we discuss the intricacies of digital influence we should also keep in mind the majority of influence occurs in the analog world. I’ve seen estimates ranging from 70 – 90% of influence occurring by offline WOM. It’s personal. It’s about real family and friends and not Twitter friends. Influence is about a relatively small number of people (Dunbar’s Number suggests humans have a finite cognitive capacity to have around 150 social relationships with other humans), and not mass influence. The fact that most influence happens offline presents another significant measurement challenge.

In summary, I’ll leave you with a few sound bites on social media influence:

At the 3rd European Summit on Measurement held in Lisbon in June 2011, standardization, education, ROI and measurement ubiquity emerged as the key themes in response to a call to set the Measurement Agenda 2020. Delegates to the conference voted on 12 priorities they thought were most important to focus on in the period leading up to 2020. The top four vote-getters became the Measurement Agenda 2020:

How to measure the return on investment of public relations (89%)

Create and adopt global standards for social media measurement (83%)

Measurement of PR campaigns and programs needs to become an intrinsic part of the PR toolkit (73%)

Institute a client education program such that clients insist on measurement of outputs, outcomes and business results from PR programs (61%)

For a very nice overview of the Lisbon session and the Barcelona Principles that came before, read this post from Dr. David Rockland of Ketchum who chaired the Barcelona and Lisbon sessions. David pretty much said it all on these sessions, so I’ll just add a couple of comments and share a few thoughts on what I believe the future of measurement 2020 could be.

The rallying cry coming out of Barcelona has been focused and loud – death to AVEs! Will there be a similar thematic coming out of Lisbon and what might it be? My money is on standardization, borne out of cross-industry cooperation. As David points out in his post, and in the words of AMEC Chairman Mike Daniels, “The Summit identified some significant challenges for the PR profession to address by 2020. However, what we also accomplished in Lisbon beyond setting the priorities was to harness the commitment and energy of the industry to agree what we need to do together.” The current cooperation and collaboration between industry groups – AMEC, Institute for Public Relations, PRSA and the Council of PR Firms is unprecedented in my time in this industry and is focused on tangible outcomes. Cross-organization committees are already at work developing standard metrics for social media measurement for example. The spirit of cooperation is uplifting. While the outward thematic appears to be standardization, cooperation is the enabling force.

I was also struck by the symmetry of the call to end AVEs in Barcelona and the call to codify ways to measure ROI in Lisbon. One follows the other. In my opinion the primary reason AVEs exist is because PR practitioners feel pressure to prove the value of what they do, and quite often they are asked to describe the impact in financial terms. AVEs are perceived as a path of least resistance way to express financial value. Except, as we all know, AVEs don’t really have anything to do with the impact public relations creates. They are a misguided proxy for financial value. Hence the need for research-based methods to determine true return on investment.

All of the priorities coming out of Lisbon are excellent goals for the industry. And like David Rockland, I believe they will be achieved, and be achieved before 2020. Here are three other items on my wish list for Measurement 2020:

Word of Mouth/Word of Mouse Integration: For those of us focused in social media and other digital technologies, we can’t allow our digital lens to color what is fundamentally an analog world. Research studies suggest the majority of word of mouth happens in real life. From an influence perspective, I don’t think too many would argue that word of mouth from a trusted friend or family member is more powerful than word of mouse from someone you follow on Twitter. Digital cross-platform research is difficult enough, but when one huge platform is ‘real life’, we have significant challenges in measurement. WOMMA and others have made early attempts to define measurement approaches for offline WOM, but much work remains. We need ways to assess its impact and then we need to think about ways to attribute value to that impact. Mobile is a wild card here as it becomes the preferred platform for online activity. The need to triangulate online, mobile and ‘real life’ measurement presents significant challenges today, and may still by 2020.

Cookie Wars: We all know the measurement versus privacy showdown is coming, right? The first shots have already been fired. The collection of source-level personal data, enabled by cookies, is crucial to measurement and insights but has the potential for misuse or unintended disclosure. Some sophisticated consumers have had their fill of cookies. Although the broader issue might be framed as social sharing versus privacy control, how it plays out will have a direct impact on digital analytics and measurement.

Integrated Measurement across the Paid Earned Shared Owned (PESO) Spectrum: Measurement has increasingly become integrated. It began with integrated traditional (Earned) and social media (Shared) measurement and then progressed rapidly to Earned, Owned and Shared, which is where most integrated measurement programs are today. Many leading-edge integrated programs today also include advertising or Paid media. By 2020, integrated measurement across the PESO spectrum will most likely be the norm and not the exception. A key enabling element here in my view is some base level of agreement on how each area should be measured and standard metrics for each. It will take significant cooperation between industry groups, vendors, agencies and major customers/clients for cross-discipline standardization to move forward effectively. We are at the beginning of this movement in 2011. By 2020, it will be fascinating to look back and see how all this plays out.

When looking ahead to 2020, I am reminded of a measurement discussion pulled together by PRWeek a couple of years ago. Many of the Measurati attended. In response to a question of where measurement will be in five years, David Rockland replied (paraphrasing here), ‘Who knows? Five years ago who would have guessed we would all be focused on how to measure social media?’ So, there is a certain fantasy element to discussing 2020 challenges in measurement. What are your measurement fantasies?

It is not difficult to find a social media listening platform – there are over 100 to choose from. What is difficult is to find the right tool. It takes a keen understanding of scope and requirements. It takes an evaluation and selection process that will surface the best platform to fully meet your requirements. And it takes a well thought-out process for deploying the platform across the organization in an effective and efficient manner. There are many questions to be asked and answers to be given. Asking the right questions at the right time is crucial.

It is helpful to think of the overall listening platform selection process in three phases:

MetricsMan is the personal blog of Don Bartholomew, SVP Digital and Social Media Research at Ketchum. Everything posted on this blog is his personal opinion and does not necessarily represent the views of Ketchum or its clients.